Abstract
A causal, stochastic model of networked computers, based on information theory and nonequilibrium dynamical systems is presented. This provides a simple explanation for recent experimental results revealing the structure of information in network transactions. The model is based on non-Poissonian stochastic variables and pseudo-periodic functions. It explains the measured patterns seen in resource variables on computers in network communities. Weakly non-Poissonian behavior can be eliminated by a conformal scaling transformation and leads to a mapping onto statistical field theory. From this, it is possible to calculate the exact profile of the spectrum of fluctuations. This work has applications to anomaly detection and time-series analysis of computer transactions.
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